Abstract
Rapid, reliable, and continuous information is an essential component in disaster monitoring and management. Remote sensing data could be a solution, but often cannot provide continuous data due to an absence of global coverage and weather and daylight dependency. To overcome these challenges, this study makes use of weather and day/light independent Sentinel-1 data with a global coverage to monitor localized effects of different types of disasters using the Coherence Change-Detection (CCD) technique. Coherence maps were generated from Synthetic Aperture Radar (SAR) images and used to classify areas of change and no change in six study areas. These sites are located in Syria, Puerto Rico, California, and Iran. The study areas were divided into street blocks, and the standard deviation was calculated for the coherence images for each street block over entire image stacks. The study areas were classified by land-use type to reveal the spatial variation in coherence loss after a disaster. While temporal decorrelation exhibits a general loss in coherence over time, disaster occurrence, however, indicates a significant loss in coherence after an event. The variations of each street block from the average coherence for the entire image stack, as measured by a high standard deviation after a particular disaster, is an indication of disaster induced building damage.
Highlights
Remote sensing data provides a solution for rapid data acquisition during disaster scenarios as well as post disaster information needed for recovery monitoring and management
Case Studies for Detecting Changes Using Coherence Change-Detection (CCD) This section presents the results of an analysis of averaging and standard deviation values calculated from the coherence images generated to detect changes in the six study areas
RemotTe Sheinss.s2e0c18ti,o10n, xpFrOesRePnEtEsRtRhEeVrIEeWsults of an analysis of averaging and standard deviation va8loufe2s2 calculated from the coherence images generated to detect changes in the six study areas
Summary
Remote sensing data provides a solution for rapid data acquisition during disaster scenarios as well as post disaster information needed for recovery monitoring and management. The vast array of techniques that remote sensing technology offers when used alongside Geographical Information System (GIS) software, can reduce uncertainty and serve as a catalyzing agent for analyzing and sharing information [1]. Efforts have been made to monitor areas affected by disasters using Synthetic Aperture Radar (SAR) imagery [2,3,4,5,6]. These approaches, have not considered the globally available data products realized by the Sentinel-1 mission. The global availability of these data permits disaster monitoring anywhere on the planet, even in areas that lack other types of remote sensing data
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